1
|
Bai J, He M, Gao E, Yang G, Zhang C, Yang H, Dong J, Ma X, Gao Y, Zhang H, Yan X, Zhang Y, Cheng J, Zhao G. High-performance presurgical differentiation of glioblastoma and metastasis by means of multiparametric neurite orientation dispersion and density imaging (NODDI) radiomics. Eur Radiol 2024:10.1007/s00330-024-10686-8. [PMID: 38485749 DOI: 10.1007/s00330-024-10686-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 02/06/2024] [Accepted: 02/10/2024] [Indexed: 04/19/2024]
Abstract
OBJECTIVES To evaluate the performance of multiparametric neurite orientation dispersion and density imaging (NODDI) radiomics in distinguishing between glioblastoma (Gb) and solitary brain metastasis (SBM). MATERIALS AND METHODS In this retrospective study, NODDI images were curated from 109 patients with Gb (n = 57) or SBM (n = 52). Automatically segmented multiple volumes of interest (VOIs) encompassed the main tumor regions, including necrosis, solid tumor, and peritumoral edema. Radiomics features were extracted for each main tumor region, using three NODDI parameter maps. Radiomics models were developed based on these three NODDI parameter maps and their amalgamation to differentiate between Gb and SBM. Additionally, radiomics models were constructed based on morphological magnetic resonance imaging (MRI) and diffusion imaging (diffusion-weighted imaging [DWI]; diffusion tensor imaging [DTI]) for performance comparison. RESULTS The validation dataset results revealed that the performance of a single NODDI parameter map model was inferior to that of the combined NODDI model. In the necrotic regions, the combined NODDI radiomics model exhibited less than ideal discriminative capabilities (area under the receiver operating characteristic curve [AUC] = 0.701). For peritumoral edema regions, the combined NODDI radiomics model achieved a moderate level of discrimination (AUC = 0.820). Within the solid tumor regions, the combined NODDI radiomics model demonstrated superior performance (AUC = 0.904), surpassing the models of other VOIs. The comparison results demonstrated that the NODDI model was better than the DWI and DTI models, while those of the morphological MRI and NODDI models were similar. CONCLUSION The NODDI radiomics model showed promising performance for preoperative discrimination between Gb and SBM. CLINICAL RELEVANCE STATEMENT The NODDI radiomics model showed promising performance for preoperative discrimination between Gb and SBM, and radiomics features can be incorporated into the multidimensional phenotypic features that describe tumor heterogeneity. KEY POINTS • The neurite orientation dispersion and density imaging (NODDI) radiomics model showed promising performance for preoperative discrimination between glioblastoma and solitary brain metastasis. • Compared with other tumor volumes of interest, the NODDI radiomics model based on solid tumor regions performed best in distinguishing the two types of tumors. • The performance of the single-parameter NODDI model was inferior to that of the combined-parameter NODDI model.
Collapse
Affiliation(s)
- Jie Bai
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
- Henan Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment, Zhengzhou, 450052, China
| | - Mengyang He
- School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou, 450001, China
| | - Eryuan Gao
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
- Henan Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment, Zhengzhou, 450052, China
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, 200062, China
| | - Chengxiu Zhang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, 200062, China
| | - Hongxi Yang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, 200062, China
| | - Jie Dong
- School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China
| | - Xiaoyue Ma
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
- Henan Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment, Zhengzhou, 450052, China
| | - Yufei Gao
- School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou, 450001, China
| | - Huiting Zhang
- MR Research Collaboration, Siemens Healthineers, Wuhan, 201318, China
| | - Xu Yan
- MR Research Collaboration, Siemens Healthineers, Wuhan, 201318, China
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
- Henan Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment, Zhengzhou, 450052, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
- Henan Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment, Zhengzhou, 450052, China
| | - Guohua Zhao
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China.
- Henan Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment, Zhengzhou, 450052, China.
| |
Collapse
|
2
|
Aznarez-Sanado M, Romero-Garcia R, Li C, Morris RC, Price SJ, Manly T, Santarius T, Erez Y, Hart MG, Suckling J. Brain tumour microstructure is associated with post-surgical cognition. Sci Rep 2024; 14:5646. [PMID: 38454017 PMCID: PMC10920778 DOI: 10.1038/s41598-024-55130-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 02/20/2024] [Indexed: 03/09/2024] Open
Abstract
Brain tumour microstructure is potentially predictive of changes following treatment to cognitive functions subserved by the functional networks in which they are embedded. To test this hypothesis, intra-tumoural microstructure was quantified from diffusion-weighted MRI to identify which tumour subregions (if any) had a greater impact on participants' cognitive recovery after surgical resection. Additionally, we studied the role of tumour microstructure in the functional interaction between the tumour and the rest of the brain. Sixteen patients (22-56 years, 7 females) with brain tumours located in or near speech-eloquent areas of the brain were included in the analyses. Two different approaches were adopted for tumour segmentation from a multishell diffusion MRI acquisition: the first used a two-dimensional four group partition of feature space, whilst the second used data-driven clustering with Gaussian mixture modelling. For each approach, we assessed the capability of tumour microstructure to predict participants' cognitive outcomes after surgery and the strength of association between the BOLD signal of individual tumour subregions and the global BOLD signal. With both methodologies, the volumes of partially overlapped subregions within the tumour significantly predicted cognitive decline in verbal skills after surgery. We also found that these particular subregions were among those that showed greater functional interaction with the unaffected cortex. Our results indicate that tumour microstructure measured by MRI multishell diffusion is associated with cognitive recovery after surgery.
Collapse
Affiliation(s)
- Maite Aznarez-Sanado
- School of Education and Psychology, University of Navarra, 31009, Pamplona, Spain
| | - Rafael Romero-Garcia
- Department of Medical Physiology and Biophysics, Instituto de Biomedicina de Sevilla (IBiS), HUVR/CSIC/Universidad de Sevilla/CIBERSAM, ISCIII, 41013, Sevilla, Spain.
- Department of Psychiatry, University of Cambridge, Herchel Smith Bldg, Robinson Way, Cambridge, CB2 0SZ, UK.
| | - Chao Li
- Cambridge Brain Tumour Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK
- Department of Applied Mathematics and Theoretical Physics, The Centre for Mathematical Imaging in Healthcare, Cambridge, CB3 0WA, UK
- School of Medicine & School of Science and Engineering, University of Dundee, Dundee, DD1 4HN, UK
| | - Rob C Morris
- Academic Neurosurgery Division, Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Stephen J Price
- Cambridge Brain Tumour Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Thomas Manly
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, CB2 7EF, UK
| | - Thomas Santarius
- Academic Neurosurgery Division, Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Yaara Erez
- Faculty of Engineering, Bar-Ilan University, 5290002, Ramat Gan, Israel
- The Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan, Israel
| | - Michael G Hart
- St George's, University of London and St George's University Hospitals NHS Foundation Trust, Institute of Molecular and Clinical Sciences, Neurosciences Research Centre, Cranmer Terrace, London, SW17 0RE, UK
| | - John Suckling
- Department of Psychiatry, University of Cambridge, Herchel Smith Bldg, Robinson Way, Cambridge, CB2 0SZ, UK
- Cambridge and Peterborough NHS Foundation Trust, Cambridge, CB21 5EF, UK
| |
Collapse
|
3
|
Shibata Y, Ishiyama S. Neurite Damage in Patients with Migraine. Neurol Int 2024; 16:299-311. [PMID: 38525701 PMCID: PMC10961799 DOI: 10.3390/neurolint16020021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 02/20/2024] [Accepted: 02/26/2024] [Indexed: 03/26/2024] Open
Abstract
We examined neurite orientation dispersion and density imaging in patients with migraine. We found that patients with medication overuse headache exhibited lower orientation dispersion than those without. Moreover, orientation dispersion in the body of the corpus callosum was statistically negatively correlated with migraine attack frequencies. These findings indicate that neurite dispersion is damaged in patients with chronic migraine. Our study results indicate the orientation preference of neurite damage in migraine.
Collapse
Affiliation(s)
- Yasushi Shibata
- Department of Neurosurgery, Headache Clinic, Mito Medical Center, University of Tsukuba, Mito Kyodo General Hospital, Mito 3100015, Japan
| | - Sumire Ishiyama
- Center for Medical Sciences, Ibaraki Prefectural University of Health Sciences, Ami 3000394, Japan
| |
Collapse
|
4
|
Bai J, He M, Gao E, Yang G, Yang H, Dong J, Ma X, Gao Y, Zhang H, Yan X, Zhang Y, Cheng J, Zhao G. Radiomic texture analysis based on neurite orientation dispersion and density imaging to differentiate glioblastoma from solitary brain metastasis. BMC Cancer 2023; 23:1231. [PMID: 38098041 PMCID: PMC10722697 DOI: 10.1186/s12885-023-11718-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 12/05/2023] [Indexed: 12/17/2023] Open
Abstract
BACKGROUND We created discriminative models of different regions of interest (ROIs) using radiomic texture features of neurite orientation dispersion and density imaging (NODDI) and evaluated the feasibility of each model in differentiating glioblastoma multiforme (GBM) from solitary brain metastasis (SBM). METHODS We conducted a retrospective study of 204 patients with GBM (n = 146) or SBM (n = 58). Radiomic texture features were extracted from five ROIs based on three metric maps (intracellular volume fraction, orientation dispersion index, and isotropic volume fraction of NODDI), including necrosis, solid tumors, peritumoral edema, tumor bulk volume (TBV), and abnormal bulk volume. Four feature selection methods and eight classifiers were used for the radiomic texture feature selection and model construction. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of the models. Routine magnetic resonance imaging (MRI) radiomic texture feature models generated in the same manner were used for the horizontal comparison. RESULTS NODDI-radiomic texture analysis based on TBV subregions exhibited the highest accuracy (although nonsignificant) in differentiating GBM from SBM, with area under the ROC curve (AUC) values of 0.918 and 0.882 in the training and test datasets, respectively, compared to necrosis (AUCtraining:0.845, AUCtest:0.714), solid tumor (AUCtraining:0.852, AUCtest:0.821), peritumoral edema (AUCtraining:0.817, AUCtest:0.762), and ABV (AUCtraining:0.834, AUCtest:0.779). The performance of the five ROI radiomic texture models in routine MRI was inferior to that of the NODDI-radiomic texture model. CONCLUSION Preoperative NODDI-radiomic texture analysis based on TBV subregions shows great potential for distinguishing GBM from SBM.
Collapse
Affiliation(s)
- Jie Bai
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, No. Jianshe Dong Road, Zhengzhou, 450052, China
| | - Mengyang He
- School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou, 450001, China
| | - Eryuan Gao
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, No. Jianshe Dong Road, Zhengzhou, 450052, China
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, 200062, China
| | - Hongxi Yang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, 200062, China
| | - Jie Dong
- School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China
| | - Xiaoyue Ma
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, No. Jianshe Dong Road, Zhengzhou, 450052, China
| | - Yufei Gao
- School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou, 450001, China
| | - Huiting Zhang
- MR Research Collaboration, Siemens Healthineers, Wuhan, 201318, China
| | - Xu Yan
- MR Research Collaboration, Siemens Healthineers, Wuhan, 201318, China
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, No. Jianshe Dong Road, Zhengzhou, 450052, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, No. Jianshe Dong Road, Zhengzhou, 450052, China
| | - Guohua Zhao
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, No. Jianshe Dong Road, Zhengzhou, 450052, China.
| |
Collapse
|
5
|
Lampinen B, Szczepankiewicz F, Lätt J, Knutsson L, Mårtensson J, Björkman-Burtscher IM, van Westen D, Sundgren PC, Ståhlberg F, Nilsson M. Probing brain tissue microstructure with MRI: principles, challenges, and the role of multidimensional diffusion-relaxation encoding. Neuroimage 2023; 282:120338. [PMID: 37598814 DOI: 10.1016/j.neuroimage.2023.120338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 06/30/2023] [Accepted: 08/17/2023] [Indexed: 08/22/2023] Open
Abstract
Diffusion MRI uses the random displacement of water molecules to sensitize the signal to brain microstructure and to properties such as the density and shape of cells. Microstructure modeling techniques aim to estimate these properties from acquired data by separating the signal between virtual tissue 'compartments' such as the intra-neurite and the extra-cellular space. A key challenge is that the diffusion MRI signal is relatively featureless compared with the complexity of brain tissue. Another challenge is that the tissue microstructure is wildly different within the gray and white matter of the brain. In this review, we use results from multidimensional diffusion encoding techniques to discuss these challenges and their tentative solutions. Multidimensional encoding increases the information content of the data by varying not only the b-value and the encoding direction but also additional experimental parameters such as the shape of the b-tensor and the echo time. Three main insights have emerged from such encoding. First, multidimensional data contradict common model assumptions on diffusion and T2 relaxation, and illustrates how the use of these assumptions cause erroneous interpretations in both healthy brain and pathology. Second, many model assumptions can be dispensed with if data are acquired with multidimensional encoding. The necessary data can be easily acquired in vivo using protocols optimized to minimize Cramér-Rao lower bounds. Third, microscopic diffusion anisotropy reflects the presence of axons but not dendrites. This insight stands in contrast to current 'neurite models' of brain tissue, which assume that axons in white matter and dendrites in gray matter feature highly similar diffusion. Nevertheless, as an axon-based contrast, microscopic anisotropy can differentiate gray and white matter when myelin alterations confound conventional MRI contrasts.
Collapse
Affiliation(s)
- Björn Lampinen
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden.
| | | | - Jimmy Lätt
- Department of Medical Imaging and Physiology, Skåne University Hospital Lund, Lund, Sweden
| | - Linda Knutsson
- Clinical Sciences Lund, Medical Radiation Physics, Lund University, Lund, Sweden; Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Johan Mårtensson
- Clinical Sciences Lund, Logopedics, Phoniatrics and Audiology, Lund University, Lund, Sweden
| | - Isabella M Björkman-Burtscher
- Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg and Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Danielle van Westen
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden; Department of Medical Imaging and Physiology, Skåne University Hospital Lund, Lund, Sweden
| | - Pia C Sundgren
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden; Department of Medical Imaging and Physiology, Skåne University Hospital Lund, Lund, Sweden; Lund University BioImaging Centre (LBIC), Lund University, Lund, Sweden
| | - Freddy Ståhlberg
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden; Clinical Sciences Lund, Medical Radiation Physics, Lund University, Lund, Sweden
| | - Markus Nilsson
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden
| |
Collapse
|
6
|
Xu X, Zhang P, Zhuo Z, Duan Y, Qu L, Cheng D, Sun T, Ding J, Xie C, Liu X, Haller S, Barkhof F, Ye C, Zhang L, Liu Y. Prediction of H3K27M Alteration Status in Brainstem Glioma Using Multi-Shell Diffusion MRI Metrics. J Magn Reson Imaging 2023. [PMID: 37889147 DOI: 10.1002/jmri.29104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 10/15/2023] [Accepted: 10/16/2023] [Indexed: 10/28/2023] Open
Abstract
BACKGROUND Multi-shell diffusion characteristics may help characterize brainstem gliomas (BSGs) and predict H3K27M status. PURPOSE To identify the diffusion characteristics of BSG patients and investigate the predictive values of various diffusion metrics for H3K27M status in BSG. STUDY TYPE Prospective. POPULATION Eighty-four BSG patients (median age 10.5 years [IQR 6.8-30.0 years]) were included, of whom 56 were pediatric and 28 were adult patients. FIELD STRENGTH/SEQUENCE 3 T, multi-shell diffusion imaging. ASSESSMENT Diffusion kurtosis imaging and neurite orientation dispersion and density imaging analyses were performed. Age, gender, and diffusion metrics, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity, radial diffusivity (RD), mean kurtosis (MK), axial kurtosis (AK), radial kurtosis, intracellular volume fraction (ICVF), orientation dispersion index, and isotropic volume fraction (ISOVF), were compared between H3K27M-altered and wildtype BSG patients. STATISTICAL TESTS Chi-square test, Mann-Whitney U test, multivariate analysis of variance (MANOVA), step-wise multivariable logistic regression. P-values <0.05 were considered significant. RESULTS 82.4% pediatric and 57.1% adult patients carried H3K27M alteration. In the whole group, the H3K27M-altered BSGs demonstrated higher FA, AK and lower RD, ISOVF. The combination of age and median ISOVF showed fair performance for H3K27M prediction (AUC = 0.78). In the pediatric group, H3K27M-altered BSGs showed higher FA, AK, MK, ICVF and lower RD, MD, ISOVF. The combinations of median ISOVF, 5th percentile of FA, median MK and median MD showed excellent predictive power (AUC = 0.91). In the adult group, H3K27M-altered BSGs showed higher ICVF and lower RD, MD. The 75th percentile of RD demonstrated fair performance for H3K27M status prediction (AUC = 0.75). DATA CONCLUSION Different alteration patterns of diffusion measures were identified between H3K27M-altered and wildtype BSGs, which collectively had fair to excellent predictive value for H3K27M alteration status, especially in pediatric patients. EVIDENCE LEVEL 2 TECHNICAL EFFICACY: Stage 3.
Collapse
Affiliation(s)
- Xiaolu Xu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Peng Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhizheng Zhuo
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yunyun Duan
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Liying Qu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Dan Cheng
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Ting Sun
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jinli Ding
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Cong Xie
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xing Liu
- Department of Pathology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Sven Haller
- Department of Imaging and Medical Informatics, University Hospitals of Geneva and Faculty of Medicine of the University of Geneva, Geneva, Switzerland
| | - Frederik Barkhof
- UCL Institutes of Neurology and Healthcare Engineering, London, UK
- Department of Radiology & Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Chuyang Ye
- School of Information and Electronics, Beijing Institute of Technology, Beijing, China
| | - Liwei Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yaou Liu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| |
Collapse
|
7
|
Zeng S, Ma H, Xie D, Huang Y, Wang M, Zeng W, Zhu N, Ma Z, Yang Z, Chu J, Zhao J. Quantitative susceptibility mapping evaluation of glioma. Eur Radiol 2023; 33:6636-6647. [PMID: 37095360 DOI: 10.1007/s00330-023-09647-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 12/28/2022] [Accepted: 02/24/2023] [Indexed: 04/26/2023]
Abstract
OBJECTIVES To comprehensively evaluate the glioma using quantitative susceptibility mapping (QSM). MATERIALS AND METHODS Forty-two patients (18 women; mean age, 45 years) with pathologically confirmed gliomas were retrospectively included. All the patients underwent conventional and advanced MRI examinations (QSM, DWI, MRS, etc.). Five patients underwent paired QSM (pre- and post-enhancement). Four Visually Accessible Rembrandt Image (VASARI) features and intratumoural susceptibility signal (ITSS) were observed. Three ROIs each were manually drawn separately in the tumour parenchyma with relatively high and low magnetic susceptibility. The association between the tumour's magnetic susceptibility and other MRI parameters was also analysed. RESULTS Morphologically, gliomas with heterogeneous ITSS were more similar to high-grade gliomas (p = 0.006, AUC: 0.72, sensitivity: 70%, and specificity: 73%). Heterogeneous ITSS was significantly associated with tumour haemorrhage, necrosis, diffusion restriction, and avid enhancement but did not change between pre- and post-enhanced QSM. Quantitatively, tumour parenchyma magnetic susceptibility had limited value in grading gliomas and identifying IDH mutation status, whereas the relatively low magnetic susceptibility of the tumour parenchyma helped identify oligodendrogliomas in IDH mutated gliomas (AUC = 0.78) with high specificity (100%). The relatively high tumour magnetic susceptibility significantly increased after enhancement (p = 0.039). Additionally, we found that the magnetic susceptibility of the tumour parenchyma was significantly correlated with ADC (r = 0.61) and Cho/NAA (r = 0.40). CONCLUSIONS QSM is a promising candidate for the comprehensive evaluation of gliomas, except for IDH mutation status. The magnetic susceptibility of tumour parenchyma may be affected by tumour cell proliferation. KEY POINTS • Morphologically, gliomas with a heterogeneous intratumoural susceptibility signal (ITSS) are more similar to high-grade gliomas (p = 0.006; AUC, 0.72; sensitivity, 70%; and specificity, 73%). Heterogeneous ITSS was significantly associated with tumour haemorrhage, necrosis, diffusion restriction, and avid enhancement but did not change between pre- and post-enhanced QSM. • Tumour parenchyma's relatively low magnetic susceptibility helped identify oligodendroglioma with high specificity. • Tumour parenchyma magnetic susceptibility was significantly correlated with ADC (r = 0.61) and Cho/NAA (r = 0.40).
Collapse
Affiliation(s)
- Shanmei Zeng
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, No. 58 Zhongshan Road 2, Guangdong, 510080, Guangzhou, People's Republic of China
| | - Hui Ma
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, No. 58 Zhongshan Road 2, Guangdong, 510080, Guangzhou, People's Republic of China
| | - Dingxiang Xie
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, No. 58 Zhongshan Road 2, Guangdong, 510080, Guangzhou, People's Republic of China
| | - Yingqian Huang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, No. 58 Zhongshan Road 2, Guangdong, 510080, Guangzhou, People's Republic of China
| | - Mengzhu Wang
- Department of MR Scientific Marketing, Siemens Healthineers, Guangzhou, Guangdong, People's Republic of China
| | - Wenting Zeng
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, No. 58 Zhongshan Road 2, Guangdong, 510080, Guangzhou, People's Republic of China
| | - Nengjin Zhu
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, No. 58 Zhongshan Road 2, Guangdong, 510080, Guangzhou, People's Republic of China
| | - Zuliwei Ma
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, No. 58 Zhongshan Road 2, Guangdong, 510080, Guangzhou, People's Republic of China
| | - Zhiyun Yang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, No. 58 Zhongshan Road 2, Guangdong, 510080, Guangzhou, People's Republic of China
| | - Jianping Chu
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, No. 58 Zhongshan Road 2, Guangdong, 510080, Guangzhou, People's Republic of China.
| | - Jing Zhao
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, No. 58 Zhongshan Road 2, Guangdong, 510080, Guangzhou, People's Republic of China.
| |
Collapse
|
8
|
She D, Huang H, Guo W, Jiang D, Zhao X, Kang Y, Cao D. Grading meningiomas with diffusion metrics: a comparison between diffusion kurtosis, mean apparent propagator, neurite orientation dispersion and density, and diffusion tensor imaging. Eur Radiol 2023; 33:3671-3681. [PMID: 36897347 DOI: 10.1007/s00330-023-09505-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 01/07/2023] [Accepted: 01/30/2023] [Indexed: 03/11/2023]
Abstract
OBJECTIVES To compare the histogram features of multiple diffusion metrics in predicting the grade and cellular proliferation of meningiomas. METHODS Diffusion spectrum imaging was performed in 122 meningiomas (30 males, 13-84 years), which were divided into 31 high-grade meningiomas (HGMs, grades 2 and 3) and 91 low-grade meningiomas (LGMs, grade 1). The histogram features of multiple diffusion metrics obtained from diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), mean apparent propagator (MAP), and neurite orientation dispersion and density imaging (NODDI) in the solid tumours were analysed. All values between the two groups were compared with the Man-Whitney U test. Logistic regression analysis was applied to predict meningioma grade. The correlation between diffusion metrics and Ki-67 index was analysed. RESULTS The DKI_AK (axial kurtosis) maximum, DKI_AK range, MAP_RTPP (return-to-plane probability) maximum, MAP_RTPP range, NODDI_ICVF (intracellular volume fraction) range, and NODDI_ICVF maximum values were lower (p < 0.0001), whilst the DTI_MD (mean diffusivity) minimum values were higher in LGMs than those in HGMs (p < 0.001). Amongst the DTI, DKI, MAP, NODDI, and combined diffusion models, no significant differences were found in areas under the receiver operating characteristic curves (AUCs) for grading meningiomas (AUCs, 0.75, 0.75, 0.80, 0.79, and 0.86, respectively; all corrected p > 0.05, Bonferroni correction). Significant but weak positive correlations were found between the Ki-67 index and DKI, MAP, and NODDI metrics (r = 0.26-0.34, all p < 0.05). CONCLUSIONS Whole tumour histogram analyses of the multiple diffusion metrics from four diffusion models are promising methods in grading meningiomas. The DTI model has similar diagnostic performance compared with advanced diffusion models. KEY POINTS • Whole tumour histogram analyses of multiple diffusion models are feasible for grading meningiomas. • The DKI, MAP, and NODDI metrics are weakly associated with the Ki-67 proliferation status. • DTI has similar diagnostic performance compared with DKI, MAP, and NODDI in grading meningiomas.
Collapse
Affiliation(s)
- Dejun She
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, Fujian, 350005, People's Republic of China
- Key Laboratory of Radiation Biology of Fujian Higher Education Institutions, the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, 350005, People's Republic of China
| | - Hao Huang
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, Fujian, 350005, People's Republic of China
| | - Wei Guo
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, Fujian, 350005, People's Republic of China
| | - Dongmei Jiang
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, Fujian, 350005, People's Republic of China
| | - Xiance Zhao
- Philips, Healthineers Ltd., Beijing, 100000, People's Republic of China
| | - Yun Kang
- Philips, Healthineers Ltd., Beijing, 100000, People's Republic of China
| | - Dairong Cao
- Department of Radiology, First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, Fujian, 350005, People's Republic of China.
- Key Laboratory of Radiation Biology of Fujian Higher Education Institutions, the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, 350005, People's Republic of China.
- Department of Radiology, Fujian Key Laboratory of Precision Medicine for Cancer, the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, 350005, People's Republic of China.
| |
Collapse
|
9
|
Guo H, Liu J, Hu J, Zhang H, Zhao W, Gao M, Zhang Y, Yang G, Cui Y. Diagnostic performance of gliomas grading and IDH status decoding A comparison between 3D amide proton transfer APT and four diffusion-weighted MRI models. J Magn Reson Imaging 2022; 56:1834-1844. [PMID: 35488516 PMCID: PMC9790544 DOI: 10.1002/jmri.28211] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 04/13/2022] [Accepted: 04/14/2022] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND The focus of neuro-oncology research has changed from histopathologic grading to molecular characteristics, and medical imaging routinely follows this change. PURPOSE To compare the diagnostic performance of amide proton transfer (APT) and four diffusion models in gliomas grading and isocitrate dehydrogenase (IDH) genotype. STUDY TYPE Prospective. POPULATION A total of 62 participants (37 males, 25 females; mean age, 52 ± 13 years) whose IDH genotypes were mutant in 6 of 14 grade II gliomas, 8 of 20 of grade III gliomas, and 4 of 28 grade IV gliomas. FIELD STRENGTH/SEQUENCE APT imaging using sampling perfection with application optimized contrasts by using different flip angle evolutions (SPACE) and DWI with q-space Cartesian grid sampling were acquired at 3 T. ASSESSMENT The ability of diffusion kurtosis imaging, diffusion kurtosis imaging, neurite orientation dispersion and density imaging (NODDI), mean apparent propagator (MAP), and APT imaging for glioma grade and IDH status were assessed, with histopathological grade and genetic testing used as a reference standard. Regions of interest (ROIs) were drawn by two neuroradiologists after consensus. STATISTICAL TESTS T-test and Mann-Whitney U test; one-way analysis of variance (ANOVA); receiver operating curve (ROC) and area under the curve (AUC); DeLong test. P value < 0.05 was considered statistically significant. RESULTS Compared with IDH-mutant gliomas, IDH-wildtype gliomas showed a significantly higher mean, 5th-percentile (APT5 ), and 95th-percentile from APTw, the 95th-percentile value of axial, mean, and radial diffusivity from DKI, and 95th-percentile value of isotropic volume fraction from NODDI, and no significantly different parameters from DTI and MAP (P = 0.075-0.998). The combined APT model showed a significantly wider area under the curve (AUC 0.870) for IDH status, when compared with DKI and NODDI. APT5 was significantly different between two of the three groups (glioma II vs. glioma III vs. glioma IV: 1.35 ± 0.75 vs. 2.09 ± 0.93 vs. 2.71 ± 0.81). DATA CONCLUSION APT has higher diagnostic accuracy than DTI, DKI, MAP, and NODDI in glioma IDH genotype. APT5 can effectively identify both tumor grading and IDH genotyping, making it a promising biomarker for glioma classification. EVIDENCE LEVEL 1 TECHNICAL EFFICACY: Stage 2.
Collapse
Affiliation(s)
- Hu Guo
- Department of RadiologyThe Second Xiangya Hospital, Central South UniversityNo. 139 Middle Renmin Road, ChangshaHunan410011China
| | - Jun Liu
- Department of RadiologyThe Second Xiangya Hospital, Central South UniversityNo. 139 Middle Renmin Road, ChangshaHunan410011China,Department of Radiology Quality Control CenterHunan ProvinceChangsha410011China
| | - JunJiao Hu
- Department of RadiologyThe Second Xiangya Hospital, Central South UniversityNo. 139 Middle Renmin Road, ChangshaHunan410011China
| | - HuiTing Zhang
- MR Scientific Marketing, Siemens Healthineers Ltd.Wuhan430071China
| | - Wei Zhao
- Department of RadiologyThe Second Xiangya Hospital, Central South UniversityNo. 139 Middle Renmin Road, ChangshaHunan410011China
| | - Min Gao
- Department of RadiologyThe Second Xiangya Hospital, Central South UniversityNo. 139 Middle Renmin Road, ChangshaHunan410011China
| | - Yi Zhang
- Department of Biomedical EngineeringCollege of Biomedical Engineering & Instrument Science, Zhejiang UniversityHangzhouZhejiangChina
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic ResonanceSchool of Physics and Electronic, East China Normal UniversityShanghaiChina
| | - Yan Cui
- Department of NeurosurgeryThe Second Xiangya Hospital, Central South UniversityNo. 139 Middle Renmin Rd, ChangshaHunan Province410011P.R. China
| |
Collapse
|
10
|
Evaluation of Temozolomide Treatment for Glioblastoma Using Amide Proton Transfer Imaging and Diffusion MRI. Cancers (Basel) 2022; 14:cancers14081907. [PMID: 35454814 PMCID: PMC9031574 DOI: 10.3390/cancers14081907] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 04/06/2022] [Indexed: 01/27/2023] Open
Abstract
Simple Summary Glioblastoma (GBM), the most frequent and malignant histological type of glioma, is associated with a very high mortality rate. MRI is a useful method for the evaluation of tumor growth. However, there are few studies that have quantitatively evaluated the changes in disease state after TMZ treatment against GBM, and it is not fully understood how the effects of treatment are reflected in the quantitative values measured on MRI. We used the C6 glioma rat model to evaluate the tumor changes due to chemotherapy at different doses of TMZ in terms of quantitative values measured by neurite orientation dispersion and density imaging (NODDI) and amide proton transfer (APT) imaging using 7T-MRI. These methods can evaluate the microstructural changes caused by TMZ-induced tumor growth inhibition. Abstract This study aimed to evaluate tumor changes due to chemotherapy with temozolomide (TMZ) in terms of quantitative values measured by APT imaging and NODDI. We performed TMZ treatment (administered orally by gavage to the TMZ-40 mg and TMZ-60 mg groups) on 7-week-old male Wistar rats with rat glioma C6 implanted in the right brain. T2WI, APT imaging, diffusion tensor imaging (DTI), and NODDI were performed on days 7 and 14 after implantation using 7T-MRI, and the calculated quantitative values were statistically compared. Then, HE staining was performed on brain tissue at day 7 and day 14 for each group to compare the results with the MR images. TMZ treatment inhibited tumor growth and necrotic area formation. The necrotic areas observed upon hematoxylin and eosin (HE) staining were consistent with the MTR low-signal areas observed upon APT imaging. The intracellular volume fraction (ICVF) map of the NODDI could best show the microstructure of the tumor, and its value could significantly highlight the difference in treatment effects at different TMZ doses. APT imaging and NODDI can be used to detect the microstructural changes caused by TMZ-induced tumor growth inhibition. The ICVF may be useful as a parameter for determining the effect of TMZ.
Collapse
|
11
|
Champagne AA, Coverdale NS, Allen MD, Tremblay JC, MacPherson REK, Pyke KE, Olver TD, Cook DJ. The physiological basis underlying functional connectivity differences in older adults: A multi-modal analysis of resting-state fMRI. Brain Imaging Behav 2022; 16:1575-1591. [PMID: 35092574 DOI: 10.1007/s11682-021-00570-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2020] [Accepted: 09/27/2021] [Indexed: 11/02/2022]
Abstract
The purpose of this study was to determine if differences in functional connectivity strength (FCS) with age were confounded by vascular parameters including resting cerebral blood flow (CBF0), cerebrovascular reactivity (CVR), and BOLD-CBF coupling. Neuroimaging data were collected from 13 younger adults (24 ± 2 years) and 14 older adults (71 ± 4 years). A dual-echo resting state pseudo-continuous arterial spin labeling sequence was performed, as well as a BOLD breath-hold protocol. A group independent component analysis was used to identify networks, which were amalgamated into a region of interest (ROI). Within the ROI, FC strength (FCS) was computed for all voxels and compared across the groups. CBF0, CVR and BOLD-CBF coupling were examined within voxels where FCS was different between young and older adults. FCS was greater in old compared to young (P = 0.001). When the effect of CBF0, CVR and BOLD-CBF coupling on FCS was examined, BOLD-CBF coupling had a significant effect (P = 0.003) and group differences in FCS were not present once all vascular parameters were considered in the statistical model (P = 0.07). These findings indicate that future studies of FCS should consider vascular physiological markers in order to improve our understanding of aging processes on brain connectivity.
Collapse
Affiliation(s)
- Allen A Champagne
- Centre for Neuroscience Studies, Queen's University, Room 260, Kingston, ON, K7L 3N6, Canada
| | - Nicole S Coverdale
- Centre for Neuroscience Studies, Queen's University, Room 260, Kingston, ON, K7L 3N6, Canada
| | - Matti D Allen
- Department of Physical Medicine and Rehabilitation, Queen's University, Kingston, ON, Canada.,School of Kinesiology and Health Studies, Cardiovascular Stress Response Laboratory, Queen's University, Kingston, ON, K7L 3N6, Canada.,Department of Physical Medicine and Rehabilitation, Providence Care Hospital, 752 King St., Ontario, West Kingston, Canada
| | - Joshua C Tremblay
- School of Kinesiology and Health Studies, Cardiovascular Stress Response Laboratory, Queen's University, Kingston, ON, K7L 3N6, Canada
| | - Rebecca E K MacPherson
- Department of Health Sciences, Faculty of Applied Health Sciences, Brock University, 1812 Sir Isaac Brock Way, St Catharines, ON, L2S 3A1, Canada
| | - Kyra E Pyke
- School of Kinesiology and Health Studies, Cardiovascular Stress Response Laboratory, Queen's University, Kingston, ON, K7L 3N6, Canada
| | - T Dylan Olver
- Biomedical Sciences, Western College of Veterinarian Medicine, University of Saskatchewan, 52 Campus Drive, Saskatoon, Saskatchewan, S7N 5B4, Canada
| | - Douglas J Cook
- Centre for Neuroscience Studies, Queen's University, Room 260, Kingston, ON, K7L 3N6, Canada. .,Department of Surgery, Queen's University, Room 232, 18 Stuart St, Kingston, ON, K7L 3N6, Canada.
| |
Collapse
|
12
|
Sun Y, Su C, Deng K, Hu X, Xue Y, Jiang R. Mean apparent propagator-MRI in evaluation of glioma grade, cellular proliferation, and IDH-1 gene mutation status. Eur Radiol 2022; 32:3744-3754. [PMID: 35076759 DOI: 10.1007/s00330-021-08522-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Revised: 11/22/2021] [Accepted: 12/14/2021] [Indexed: 12/20/2022]
Abstract
OBJECTIVES To evaluate the glioma grade, Ki-67 expression, and IDH-1 mutation status using mean apparent propagator (MAP) MRI. METHODS Forty enrolled glioma patients underwent structural and diffusion MRI. The diffusion metric values including fractional anisotropy (FA), mean diffusivity (MD), mean squared displacement (MSD), q-space inverse variance (QIV), return-to-origin probability (RTOP), return-to-axis probability (RTAP), and return-to-plane probability (RTPP) in tumor parenchyma (TP) and contralateral normal-appearing white matter (NAWM) were calculated. The TP/NAWM ratios of diffusion metric values were correlated with tumor grades, Ki-67, and IDH-1 mutation statuses, and the diagnostic performance was assessed. RESULTS QIV were significantly higher, whereas RTAP and RTOP were significantly lower in low-grade gliomas (LGGs) than those in high-grade gliomas (HGGs); QIV and MD were significantly higher, whereas RTAP and RTOP were significantly lower in lower-grade gliomas (grade II and III) than those in grade IV gliomas (p < 0.05 for all). RTAP performed best in grading gliomas. MSD, QIV, and MD were significantly higher, whereas RTAP, RTOP, RTPP, and FA were significantly lower in the IDH-1 mutant gliomas than those in the IDH-1 wild-type ones both for all gliomas and lower-grade gliomas (p < 0.05 for all). RTAP performed best in all gliomas, while QIV performed best in lower-grade gliomas. Additionally, RTAP, RTOP, and FA correlated positively, whereas MSD, QIV, and MD correlated negatively with Ki-67 (p < 0.05 for all). CONCLUSIONS MAP-MRI is a potent approach in evaluating the microstructural changes in gliomas with different grades, cellular proliferation, and IDH-1 mutation statuses. KEY POINTS • MAP-MRI, a newly developed diffusion technique, accurately reveals microstructure-related features in the complex white matter by recovering important microstructural tissue parameters. • MAP-MRI is a potent approach in evaluating the glioma grade, IDH-1 mutation status, and Ki-67 expression. • Compared with DTI, MAP-MRI seems to demonstrate higher diagnostic performance.
Collapse
Affiliation(s)
- Yifan Sun
- Department of Radiology, Fujian Medical University Union Hospital, NO.29 Xinquan Road, Fuzhou, 350001, Fujian, People's Republic of China
| | - Changliang Su
- Department of Medical Imaging, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Kaiji Deng
- Department of Radiology, Fujian Medical University Union Hospital, NO.29 Xinquan Road, Fuzhou, 350001, Fujian, People's Republic of China
| | - Xiaomei Hu
- Department of Pathology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Yunjing Xue
- Department of Radiology, Fujian Medical University Union Hospital, NO.29 Xinquan Road, Fuzhou, 350001, Fujian, People's Republic of China
| | - Rifeng Jiang
- Department of Radiology, Fujian Medical University Union Hospital, NO.29 Xinquan Road, Fuzhou, 350001, Fujian, People's Republic of China.
| |
Collapse
|
13
|
Xie Y, Li S, Shen N, Gan T, Zhang S, Liu WV, Zhu W. Assessment of Isocitrate Dehydrogenase 1 Genotype and Cell Proliferation in Gliomas Using Multiple Diffusion Magnetic Resonance Imaging. Front Neurosci 2021; 15:783361. [PMID: 34880724 PMCID: PMC8645648 DOI: 10.3389/fnins.2021.783361] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Accepted: 10/20/2021] [Indexed: 11/13/2022] Open
Abstract
Objectives: To compare the efficacy of parameters from multiple diffusion magnetic resonance imaging (dMRI) for prediction of isocitrate dehydrogenase 1 (IDH1) genotype and assessment of cell proliferation in gliomas. Methods: Ninety-one patients with glioma underwent diffusion weighted imaging (DWI), multi-b-value DWI, and diffusion kurtosis imaging (DKI)/neurite orientation dispersion and density imaging (NODDI) on 3.0T MRI. Each parameter was compared between IDH1-mutant and IDH1 wild-type groups by Mann-Whitney U test in lower-grade gliomas (LrGGs) and glioblastomas (GBMs), respectively. Further, performance of each parameter was compared for glioma grading under the same IDH1 genotype. Spearman correlation coefficient between Ki-67 labeling index (LI) and each parameter was calculated. Results: The diagnostic performance was better achieved with apparent diffusion coefficient (ADC), slow ADC (D), fast ADC (D∗), perfusion fraction (f), distributed diffusion coefficient (DDC), heterogeneity index (α), mean diffusivity (MD), mean kurtosis (MK), and intracellular volume fraction (ICVF) for distinguishing IDH1 genotypes in LrGGs, with statistically insignificant AUC values from 0.750 to 0.817. In GBMs, no difference between the two groups was found. For IDH1-mutant group, all parameters, except for fractional anisotropy (FA) and D∗, significantly discriminated LrGGs from GBMs (P < 0.05). However, for IDH1 wild-type group, only ADC statistically discriminated the two (P = 0.048). In addition, MK has maximal correlation coefficient (r = 0.567, P < 0.001) with Ki-67 LI. Conclusion: dMRI-derived parameters are promising biomarkers for predicting IDH1 genotype in LrGGs, and MK has shown great potential in assessing glioma cell proliferation.
Collapse
Affiliation(s)
- Yan Xie
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shihui Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Nanxi Shen
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tongjia Gan
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shun Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Weiyin Vivian Liu
- Magnetic Resonance Research, General Electric Healthcare, Beijing, China
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| |
Collapse
|
14
|
Martinez-Heras E, Grussu F, Prados F, Solana E, Llufriu S. Diffusion-Weighted Imaging: Recent Advances and Applications. Semin Ultrasound CT MR 2021; 42:490-506. [PMID: 34537117 DOI: 10.1053/j.sult.2021.07.006] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Quantitative diffusion imaging techniques enable the characterization of tissue microstructural properties of the human brain "in vivo", and are widely used in neuroscientific and clinical contexts. In this review, we present the basic physical principles behind diffusion imaging and provide an overview of the current diffusion techniques, including standard and advanced techniques as well as their main clinical applications. Standard diffusion tensor imaging (DTI) offers sensitivity to changes in microstructure due to diseases and enables the characterization of single fiber distributions within a voxel as well as diffusion anisotropy. Nonetheless, its inability to represent complex intravoxel fiber topologies and the limited biological specificity of its metrics motivated the development of several advanced diffusion MRI techniques. For example, high-angular resolution diffusion imaging (HARDI) techniques enabled the characterization of fiber crossing areas and other complex fiber topologies in a single voxel and supported the development of higher-order signal representations aiming to decompose the diffusion MRI signal into distinct microstructure compartments. Biophysical models, often known by their acronym (e.g., CHARMED, WMTI, NODDI, DBSI, DIAMOND) contributed to capture the diffusion properties from each of such tissue compartments, enabling the computation of voxel-wise maps of axonal density and/or morphology that hold promise as clinically viable biomarkers in several neurological and neuroscientific applications; for example, to quantify tissue alterations due to disease or healthy processes. Current challenges and limitations of state-of-the-art models are discussed, including validation efforts. Finally, novel diffusion encoding approaches (e.g., b-tensor or double diffusion encoding) may increase the biological specificity of diffusion metrics towards intra-voxel diffusion heterogeneity in clinical settings, holding promise in neurological applications.
Collapse
Affiliation(s)
- Eloy Martinez-Heras
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona. Barcelona. Spain.
| | - Francesco Grussu
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain; Queen Square MS Center, Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Ferran Prados
- Queen Square MS Center, Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK; Center for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK; E-health Center, Universitat Oberta de Catalunya. Barcelona. Spain
| | - Elisabeth Solana
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona. Barcelona. Spain
| | - Sara Llufriu
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona. Barcelona. Spain
| |
Collapse
|
15
|
Romero-Garcia R, Suckling J, Owen M, Assem M, Sinha R, Coelho P, Woodberry E, Price SJ, Burke A, Santarius T, Erez Y, Hart MG. Memory recovery in relation to default mode network impairment and neurite density during brain tumor treatment. J Neurosurg 2021; 136:358-368. [PMID: 34359041 DOI: 10.3171/2021.1.jns203959] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 01/25/2021] [Indexed: 11/06/2022]
Abstract
OBJECTIVE The aim of this study was to test brain tumor interactions with brain networks, thereby identifying protective features and risk factors for memory recovery after resection. METHODS Seventeen patients with diffuse nonenhancing glioma (ages 22-56 years) underwent longitudinal MRI before and after surgery, and during a 12-month recovery period (47 MRI scans in total after exclusion). After each scanning session, a battery of memory tests was performed using a tablet-based screening tool, including free verbal memory, overall verbal memory, episodic memory, orientation, forward digit span, and backward digit span. Using structural MRI and neurite orientation dispersion and density imaging (NODDI) derived from diffusion-weighted images, the authors estimated lesion overlap and neurite density, respectively, with brain networks derived from normative data in healthy participants (somatomotor, dorsal attention, ventral attention, frontoparietal, and default mode network [DMN]). Linear mixed-effect models (LMMs) that regressed out the effect of age, gender, tumor grade, type of treatment, total lesion volume, and total neurite density were used to test the potential longitudinal associations between imaging markers and memory recovery. RESULTS Memory recovery was not significantly associated with either the tumor location based on traditional lobe classification or the type of treatment received by patients (i.e., surgery alone or surgery with adjuvant chemoradiotherapy). Nonlocal effects of tumors were evident on neurite density, which was reduced not only within the tumor but also beyond the tumor boundary. In contrast, high preoperative neurite density outside the tumor but within the DMN was associated with better memory recovery (LMM, p value after false discovery rate correction [Pfdr] < 10-3). Furthermore, postoperative and follow-up neurite density within the DMN and frontoparietal network were also associated with memory recovery (LMM, Pfdr = 0.014 and Pfdr = 0.001, respectively). Preoperative tumor and postoperative lesion overlap with the DMN showed a significant negative association with memory recovery (LMM, Pfdr = 0.002 and Pfdr < 10-4, respectively). CONCLUSIONS Imaging biomarkers of cognitive recovery and decline can be identified using NODDI and resting-state networks. Brain tumors and their corresponding treatment affecting brain networks that are fundamental for memory functioning such as the DMN can have a major impact on patients' memory recovery.
Collapse
Affiliation(s)
| | - John Suckling
- 1Department of Psychiatry, University of Cambridge.,2Behavioural and Clinical Neuroscience Institute, University of Cambridge.,3Cambridge and Peterborough NHS Foundation Trust, Cambridge
| | - Mallory Owen
- 1Department of Psychiatry, University of Cambridge
| | - Moataz Assem
- 4MRC Cognition and Brain Sciences Unit, University of Cambridge
| | | | | | - Emma Woodberry
- 7Department of Neuropsychology, Cambridge University Hospitals NHS Foundation Trust, Cambridge
| | - Stephen J Price
- 5Department of Neurosurgery, Addenbrooke's Hospital, Cambridge
| | - Amos Burke
- 8Department of Paediatric Haematology, Oncology, and Palliative Care, Addenbrooke's Hospital, Cambridge; and
| | - Thomas Santarius
- 5Department of Neurosurgery, Addenbrooke's Hospital, Cambridge.,9Department of Physiology, Development, and Neuroscience, University of Cambridge, Cambridgeshire, United Kingdom
| | - Yaara Erez
- 4MRC Cognition and Brain Sciences Unit, University of Cambridge
| | - Michael G Hart
- 5Department of Neurosurgery, Addenbrooke's Hospital, Cambridge
| |
Collapse
|
16
|
Jiang R, Hu X, Deng K, Jiang S, Chen W, Zhang Z. Neurite orientation dispersion and density imaging in evaluation of high-grade glioma-induced corticospinal tract injury. Eur J Radiol 2021; 140:109750. [PMID: 33991969 DOI: 10.1016/j.ejrad.2021.109750] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 04/03/2021] [Accepted: 04/29/2021] [Indexed: 11/25/2022]
Abstract
PURPOSE To evaluate the application of neurite orientation dispersion and density imaging (NODDI) to brain glioma-induced corticospinal tract (CST) injury. MATERIAL AND METHODS Twenty-four patients with high-grade glioma (HGG) in or adjacent to the CST pathway and 12 matched healthy subjects underwent structural and diffusion MRI. The CSTs were reconstructed on the both sides. The CST features including morphological features (track number, average track length and track volume) and the diffusion parameter values including fractional anisotraphy (FA), mean diffusivity (MD), intracellular volume fraction (ICVF), isotropic or free water volume fraction (ISOVF) and orientation dispersion index (ODI) along the CST were calculated. The CST features were compared between the affected and healthy side for HGG patients and between the left and right side for healthy subjects. The relative CST features were compared across the healthy subjects, patients with motor weakness and patients with normal muscle strength. Receiver operating characteristic (ROC) curve was applied to evaluate the performance of each relative CST characteristic for HGG-induced CST changes. RESULTS Compared with the CST features on the healthy side, the track number, track volume and FA along the CST changed significantly on the affected side for HGG patients (p < 0.05 for all), whereas MD and ICVF changed significantly on the affected side only for HGG patients with motor weakness (p = 0.012 for both). In patients with motor weakness, the relative MD was significantly higher (p < 0.001), whereas the relative FA and ICVF was significantly lower (p = 0.002 and <0.001) than those in patients with normal muscle strength. The relative ICVF had a similar area under curve (AUC) to that of MD (AUC=0.953 and 0.969). Compared with the relative CST features in the healthy subjects, only the relative ICVF was significantly lower in HGG patients with normal muscle strength (p = 0.012). CONCLUSIONS NODDI seems to be useful in reflecting the HGG infiltration to CST, and can evaluate the CST destruction with a performance similar to DTI by providing additional information about neurite density for HGG-induced CST injury.
Collapse
Affiliation(s)
- Rifeng Jiang
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China.
| | - Xiaomei Hu
- Department of Pathology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Kaiji Deng
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Shaofan Jiang
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Weitao Chen
- Department of Neurosurgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Zhongshuai Zhang
- MR Scientific Marketing, Siemens Healthcare, Shanghai, 201318, China
| |
Collapse
|
17
|
Mao J, Zeng W, Zhang Q, Yang Z, Yan X, Zhang H, Wang M, Yang G, Zhou M, Shen J. Differentiation between high-grade gliomas and solitary brain metastases: a comparison of five diffusion-weighted MRI models. BMC Med Imaging 2020; 20:124. [PMID: 33228564 PMCID: PMC7684933 DOI: 10.1186/s12880-020-00524-w] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Accepted: 11/16/2020] [Indexed: 12/25/2022] Open
Abstract
Background To compare the diagnostic performance of neurite orientation dispersion and density imaging (NODDI), mean apparent propagator magnetic resonance imaging (MAP-MRI), diffusion kurtosis imaging (DKI), diffusion tensor imaging (DTI) and diffusion-weighted imaging (DWI) in distinguishing high-grade gliomas (HGGs) from solitary brain metastases (SBMs). Methods Patients with previously untreated, histopathologically confirmed HGGs (n = 20) or SBMs (n = 21) appearing as a solitary and contrast-enhancing lesion on structural MRI were prospectively recruited to undergo diffusion-weighted MRI. DWI data were obtained using a q-space Cartesian grid sampling procedure and were processed to generate parametric maps by fitting the NODDI, MAP-MRI, DKI, DTI and DWI models. The diffusion metrics of the contrast-enhancing tumor and peritumoral edema were measured. Differences in the diffusion metrics were compared between HGGs and SBMs, followed by receiver operating characteristic (ROC) analysis and the Hanley and McNeill test to determine their diagnostic performances. Results NODDI-based isotropic volume fraction (Viso) and orientation dispersion index (ODI); MAP-MRI-based mean-squared displacement (MSD) and q-space inverse variance (QIV); DKI-generated radial, mean diffusivity and fractional anisotropy (RDk, MDk and FAk); and DTI-generated radial, mean diffusivity and fractional anisotropy (RD, MD and FA) of the contrast-enhancing tumor were significantly different between HGGs and SBMs (p < 0.05). The best single discriminative parameters of each model were Viso, MSD, RDk and RD for NODDI, MAP-MRI, DKI and DTI, respectively. The AUC of Viso (0.871) was significantly higher than that of MSD (0.736), RDk (0.760) and RD (0.733) (p < 0.05). Conclusion NODDI outperforms MAP-MRI, DKI, DTI and DWI in differentiating between HGGs and SBMs. NODDI-based Viso has the highest performance.
Collapse
Affiliation(s)
- Jiaji Mao
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, China.,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, China
| | - Weike Zeng
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, China
| | - Qinyuan Zhang
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, China.,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, China
| | - Zehong Yang
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, China
| | - Xu Yan
- MR Scientific Marketing, Siemens Healthcare, No. 278 Zhouzhu Road, Shanghai, 201318, China
| | - Huiting Zhang
- MR Scientific Marketing, Siemens Healthcare, No. 278 Zhouzhu Road, Shanghai, 201318, China
| | - Mengzhu Wang
- MR Scientific Marketing, Siemens Healthcare, No. 278 Zhouzhu Road, Shanghai, 201318, China
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance, Institute of Physics and Electronics Science, East China Normal University, No. 3663 North Zhongshan Road, Shanghai, 200062, China
| | - Minxiong Zhou
- College of Medical Imaging, Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, No. 279 Zhouzhu Road, Shanghai, 201318, China
| | - Jun Shen
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, China. .,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, China.
| |
Collapse
|
18
|
Chu JP, Song YK, Tian YS, Qiu HS, Huang XH, Wang YL, Huang YQ, Zhao J. Diffusion kurtosis imaging in evaluating gliomas: different region of interest selection methods on time efficiency, measurement repeatability, and diagnostic ability. Eur Radiol 2020; 31:729-739. [PMID: 32857204 DOI: 10.1007/s00330-020-07204-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Revised: 07/05/2020] [Accepted: 08/18/2020] [Indexed: 12/25/2022]
Abstract
OBJECTIVES Comparing the diagnostic efficacy of diffusion kurtosis imaging (DKI) derived from different region of interest (ROI) methods in tumor parenchyma for grading and predicting IDH-1 mutation and 1p19q co-deletion status of glioma patients and correlating with their survival data. METHODS Sixty-six patients (29 females; median age, 45 years) with pathologically proved gliomas (low-grade gliomas, 36; high-grade gliomas, 30) were prospectively included, and their clinical data were collected. All patients underwent DKI examination. DKI maps of each metric were derived. Three groups of ROIs (ten spots, ROI-10s; three biggest tumor slices, ROI-3s; and whole-tumor parenchyma, ROI-whole) were manually drawn by two independent radiologists. The interobserver consistency, time spent, diagnostic efficacy, and survival analysis of DKI metrics based on these three ROI methods were analyzed. RESULTS The intraexaminer reliability for all parameters among these three ROI methods was good, and the time spent on ROI-10s was significantly less than that of the other two methods (p < 0.001). DKI based on ROI-10s demonstrated a slightly better diagnostic value than the other two ROI methods for grading and predicting the IDH-1 mutation status of glioma, whereas DKI metrics derived from ROI-10s performed much better than those of the ROI-3s and ROI-whole in identifying 1p19q co-deletion. In survival analysis, the model based on ROI-10s that included patient age and mean diffusivity showed the highest prediction value (C-index, 0.81). CONCLUSIONS Among the three ROI methods, the ROI-10s method had the least time spent and the best diagnostic value for a comprehensive evaluation of glioma. It is an effective way to process DKI data and has important application value in the clinical evaluation of glioma. KEY POINTS • The intraexaminer reliability for all DKI parameters among different ROI methods was good, and the time spent on ROI-10 spots was significantly less than the other two ROI methods. • DKI metrics derived from ROI-10 spots performed the best in ROI selection methods (ROI-10s, ten-spot ROIs; ROI-3s, three biggest tumor slices ROI; and ROI-whole, whole-tumor parenchyma ROI) for a comprehensive evaluation of glioma. • The ROI-10 spots method is an effective way to process DKI data and has important application value in the clinical evaluation of glioma.
Collapse
Affiliation(s)
- Jian-Ping Chu
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58, The Second Zhongshan Road, Guangzhou, 510080, Guangdong, China
| | - Yu-Kun Song
- Department of Radiology, The First Affiliated Hospital of Xiamen University, Xiamen, 361003, China
| | - Yi-Su Tian
- Department of Radiology, SICHUAN Cancer Hospital and Research Institute, Chengdu, 610041, China
| | - Hai-Shan Qiu
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58, The Second Zhongshan Road, Guangzhou, 510080, Guangdong, China
| | - Xia-Hua Huang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58, The Second Zhongshan Road, Guangzhou, 510080, Guangdong, China
| | - Yu-Liang Wang
- Department of Radiology, Shenzhen City Nanshan District People's Hospital, Shenzhen, 518000, China
| | - Ying-Qian Huang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58, The Second Zhongshan Road, Guangzhou, 510080, Guangdong, China
| | - Jing Zhao
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58, The Second Zhongshan Road, Guangzhou, 510080, Guangdong, China.
| |
Collapse
|
19
|
Kamiya K, Hori M, Aoki S. NODDI in clinical research. J Neurosci Methods 2020; 346:108908. [PMID: 32814118 DOI: 10.1016/j.jneumeth.2020.108908] [Citation(s) in RCA: 116] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 08/08/2020] [Accepted: 08/09/2020] [Indexed: 12/11/2022]
Abstract
Diffusion MRI (dMRI) has proven to be a useful imaging approach for both clinical diagnosis and research investigating the microstructures of nervous tissues, and it has helped us to better understand the neurophysiological mechanisms of many diseases. Though diffusion tensor imaging (DTI) has long been the default tool to analyze dMRI data in clinical research, acquisition with stronger diffusion weightings beyond the DTI regimen is now possible with modern clinical scanners, potentially enabling even more detailed characterization of tissue microstructures. To take advantage of such data, neurite orientation dispersion and density imaging (NODDI) has been proposed as a way to relate the dMRI signal to tissue features via biophysically inspired modeling. The number of reports demonstrating the potential clinical utility of NODDI is rapidly increasing. At the same time, the pitfalls and limitations of NODDI, and general challenges in microstructure modeling, are becoming increasingly recognized by clinicians. dMRI microstructure modeling is a rapidly evolving field with great promise, where people from different scientific backgrounds, such as physics, medicine, biology, neuroscience, and statistics, are collaborating to build novel tools that contribute to improving human healthcare. Here, we review the applications of NODDI in clinical research and discuss future perspectives for investigations toward the implementation of dMRI microstructure imaging in clinical practice.
Collapse
Affiliation(s)
- Kouhei Kamiya
- Department of Radiology, The University of Tokyo, Tokyo, Japan; Department of Radiology, Juntendo University, Tokyo, Japan; Department of Radiology, Toho University, Tokyo, Japan.
| | - Masaaki Hori
- Department of Radiology, Juntendo University, Tokyo, Japan; Department of Radiology, Toho University, Tokyo, Japan
| | - Shigeki Aoki
- Department of Radiology, Juntendo University, Tokyo, Japan
| |
Collapse
|
20
|
Lee CY, Kalra A, Spampinato MV, Tabesh A, Jensen JH, Helpern JA, de Fatima Falangola M, Van Horn MH, Giglio P. Early assessment of recurrent glioblastoma response to bevacizumab treatment by diffusional kurtosis imaging: a preliminary report. Neuroradiol J 2019; 32:317-327. [PMID: 31282311 DOI: 10.1177/1971400919861409] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
PURPOSE The purpose of this preliminary study is to apply diffusional kurtosis imaging to assess the early response of recurrent glioblastoma to bevacizumab treatment. METHODS This prospective cohort study included 10 patients who had been diagnosed with recurrent glioblastoma and scheduled to receive bevacizumab treatment. Diffusional kurtosis images were obtained from all the patients 0-7 days before (pre-bevacizumab) and 28 days after (post-bevacizumab) initiating bevacizumab treatment. The mean, 10th, and 90th percentile values were derived from the histogram of diffusional kurtosis imaging metrics in enhancing and non-enhancing lesions, selected on post-contrast T1-weighted and fluid-attenuated inversion recovery images. Correlations of imaging measures with progression-free survival and overall survival were evaluated using Spearman's rank correlation coefficient. The significance level was set at P < 0.05. RESULTS Higher pre-bevacizumab non-enhancing lesion volume was correlated with poor overall survival (r = -0.65, P = 0.049). Higher post-bevacizumab mean diffusivity and axial diffusivity (D∥, D∥10% and D∥90%) in non-enhancing lesions were correlated with poor progression-free survival (r = -0.73, -0.83, -0.71 and -0.85; P < 0.05). Lower post-bevacizumab axial kurtosis (K∥10%) in non-enhancing lesions was correlated with poor progression-free survival (r = 0.81, P = 0.008). CONCLUSIONS This preliminary study demonstrates that diffusional kurtosis imaging metrics allow the detection of tissue changes 28 days after initiating bevacizumab treatment and that they may provide information about tumor progression.
Collapse
Affiliation(s)
- Chu-Yu Lee
- 1 Department of Radiology and Radiological Science, Medical University of South Carolina, USA.,2 Center for Biomedical Imaging, Medical University of South Carolina, USA
| | - Amandeep Kalra
- 3 Department of Neuroscience, Medical University of South Carolina, USA.,4 Sarah Cannon Cancer Institute, USA
| | - Maria V Spampinato
- 1 Department of Radiology and Radiological Science, Medical University of South Carolina, USA.,2 Center for Biomedical Imaging, Medical University of South Carolina, USA
| | - Ali Tabesh
- 1 Department of Radiology and Radiological Science, Medical University of South Carolina, USA.,2 Center for Biomedical Imaging, Medical University of South Carolina, USA
| | - Jens H Jensen
- 1 Department of Radiology and Radiological Science, Medical University of South Carolina, USA.,2 Center for Biomedical Imaging, Medical University of South Carolina, USA.,3 Department of Neuroscience, Medical University of South Carolina, USA
| | - Joseph A Helpern
- 1 Department of Radiology and Radiological Science, Medical University of South Carolina, USA.,2 Center for Biomedical Imaging, Medical University of South Carolina, USA.,3 Department of Neuroscience, Medical University of South Carolina, USA.,5 Department of Neurology, Medical University of South Carolina, USA
| | - Maria de Fatima Falangola
- 1 Department of Radiology and Radiological Science, Medical University of South Carolina, USA.,2 Center for Biomedical Imaging, Medical University of South Carolina, USA.,3 Department of Neuroscience, Medical University of South Carolina, USA
| | - Mark H Van Horn
- 1 Department of Radiology and Radiological Science, Medical University of South Carolina, USA.,2 Center for Biomedical Imaging, Medical University of South Carolina, USA
| | - Pierre Giglio
- 3 Department of Neuroscience, Medical University of South Carolina, USA.,6 Department of Neurology, The Ohio State University Wexner Medical Center, USA
| |
Collapse
|
21
|
Comparative analysis of the diffusion kurtosis imaging and diffusion tensor imaging in grading gliomas, predicting tumour cell proliferation and IDH-1 gene mutation status. J Neurooncol 2018; 141:195-203. [PMID: 30414095 DOI: 10.1007/s11060-018-03025-7] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Accepted: 10/03/2018] [Indexed: 10/27/2022]
Abstract
INTRODUCTION Few studies have applied diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI) for the comprehensive assessment of gliomas [tumour grade, isocitrate dehydrogenase-1 (IDH-1) mutation status and tumour proliferation rate (Ki-67)]. This study describes the efficacy of DKI and DTI to comprehensively evaluate gliomas, compares their results. METHODS Fifty-two patients (18 females; median age, 47.5 years) with pathologically proved gliomas were prospectively included. All cases underwent DKI examination. DKI (mean kurtosis: MK, axial kurtosis: Ka, radial kurtosis: Kr) and DTI (mean diffusivity: MD, fractional anisotropy: FA) maps of each metric was derived. Three ROIs were manually drawn. RESULTS MK, Ka, Kr and FA were significantly higher in HGGs than in LGGs, whereas MD was significantly lower in HGGs than in LGGs (P < 0.01). ROC analysis demonstrated that MK (specificity: 100% sensitivity: 79%) and Ka (specificity: 96% sensitivity: 82%) had the same and highest (AUC: 0.93) diagnostic value. Moreover, MK, Ka, and Kr were significantly higher in grade III than II gliomas (P ≦ 0.01). Further, DKI and DTI can significantly identify IDH-1 mutation status (P ≦ 0.03). Ka (sensitivity: 74%, specificity: 75%, AUC: 0.72) showed the highest diagnostic value. In addition, DKI metrics and MD showed significant correlations with Ki-67 (P ≦ 0.01) and Ka had the highest correlation coefficient (rs = 0.72). CONCLUSIONS Compared with DTI, DKI has great advantages for the comprehensive assessment of gliomas. Ka might serve as a promising imaging index in predicting glioma grading, tumour cell proliferation rate and IDH-1 gene mutation status.
Collapse
|